Shadow Casting Out Of Plane (SCOOP) Candidates for Human and Vehicle Detection in Aerial Imagery
Abbreviated Journal Title
Int. J. Comput. Vis.
Human detection; Vehicle detection; Aerial surveillance; UAV; Shadow; Metadata; Computer Science, Artificial Intelligence
In this paper, we propose a method for detecting humans and vehicles in imagery taken from a UAV. This is a challenging problem due to a limited number of pixels on target, which makes it more difficult to distinguish objects from background clutter, and results in much larger search space. We propose a method for constraining the search based on a number of geometric constraints obtained from the metadata. Specifically, we obtain the orientation of ground plane normal, the orientation of shadows cast by out of plane objects in the scene, and the relationship between object heights and the size of their corresponding shadows. We use the aforementioned information in a geometry-based shadow, and ground-plane normal blob detector, which provides an initial estimation for locations of shadow casting out of plane (SCOOP) objects in the scene. These SCOOP candidate locations are then classified as either human or clutter using a combination of wavelet features and a Support Vector Machine. To detect vehicles, we similarly find potential vehicle candidates by combining SCOOP and inverted-SCOOP candidates and then classify them using wavelet features and SVM. Our method works on a single frame, and unlike motion detection based methods, it bypasses the entire pipeline of registration, motion detection, and tracking. This method allows for detection of stationary and slowly moving humans and vehicles while avoiding the search across the entire image, allowing accurate and fast localization. We show impressive results on sequences from VIVID and CLIF datasets and provide comparative analysis.
International Journal of Computer Vision
"Shadow Casting Out Of Plane (SCOOP) Candidates for Human and Vehicle Detection in Aerial Imagery" (2013). Faculty Bibliography 2010s. 4589.